A cyber-physical system (CPS) integrates communication and automation technologies into the operational processes of physical systems. Nowadays, as a complex CPS, an intelligent connected vehicle (ICV) may be exposed to accidental functional failures and malicious attacks. Therefore, ensuring the ICV’s safety and security is crucial. Traditional safety/security analysis methods, such as failure mode and effect analysis and attack tree analysis, cannot provide a comprehensive analysis for the interactions between the system components of the ICV. In this work, we merge system-theoretic process analysis (STPA) with the concept phase of ISO 26262 and ISO/SAE 21434. We focus on the interactions between components while analyzing the safety and security of ICVs to reduce redundant efforts and inconsistencies in determining safety and security requirements. To conquer STPA’s abstraction in describing causal scenarios, we improved the physical component diagram of STPA-SafeSec by adding interface elements. In addition, we proposed the loss scenario tree to describe specific scenarios that lead to unsafe/unsecure control actions. After hazard/threat analysis, a unified risk assessment process is proposed to ensure consistency in assessment criteria and to streamline the process. A case study is implemented on the autonomous emergency braking system to demonstrate the validation of the proposed method.

With the rapid growth of Internet applications and network traffic, existing routing algorithms are usually difficult to guarantee the quality of service (QoS) indicators such as delay, bandwidth, and packet loss rate as well as network energy consumption for various data flows with business characteristics. They have problems such as unbalanced traffic scheduling and unreasonable network resource allocation. Aiming at the above problems, this paper proposes a QoS-oriented energy-saving routing algorithm A3C-R in the software-defined network (SDN) environment. Based on the asynchronous update advantages of the asynchronous advantage Actor-Critic (A3C) algorithm and the advantages of independent interaction between multiple agents and the environment, the A3C-R algorithm can effectively improve the convergence of the routing algorithm. The process of the A3C-R algorithm first takes QoS indicators such as delay, bandwidth, and packet loss rate and the network energy consumption of the link as input. Then, it creates multiple agents to start asynchronous training, through the continuous updating of Actors and Critics in each agent and periodically synchronizes the model parameters to the global model. After the algorithm training converges, it can output the link weights of the network topology to facilitate the calculation of intelligent routing strategies that meet QoS requirements and lower network energy consumption. The experimental results indicate that the A3C-R algorithm, compared to the baseline algorithms ECMP, I-DQN, and DDPG-EEFS, reduces delay by approximately 9.4%, increases throughput by approximately 7.0%, decreases the packet loss rate by approximately 9.5%, and improves energy-saving percentage by approximately 10.8%.